A method for joint scheduling of unmanned aerial vehicle sensing resources in a covert communication scenario
By constructing a UAV communication and radar perception model and combining it with covert communication and no-fly zone constraints, and using a block coordinate descent algorithm to solve the problem, the resource allocation problem of UAVs under covert conditions and no-fly zones was solved, thereby improving the covert communication and perception accuracy of the UAV system.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-02-03
- Publication Date
- 2026-06-23
Smart Images
Figure CN122269240A_ABST
Abstract
Description
Technical Field
[0001] This application belongs to the field of UAV communication and sensing technology, and more specifically, relates to a method for joint scheduling of UAV sensing resources in covert communication scenarios. Background Technology
[0002] With the development of 6G communication networks, communication technology is evolving from "Internet of Everything" to "Intelligent Internet of Everything," and building an integrated air-space-ground network has become a clear direction for 6G development. In this process, drones, as key aerial nodes, are gradually evolving from traditional relay and coverage enhancement platforms into intelligent aerial agents integrating communication, sensing, and computing. Drone-assisted integrated sensing and communication technologies can simultaneously utilize wireless signals to achieve information transmission and environmental perception, and are considered key enabling technologies for unlocking the potential of 6G spectrum efficiency and supporting typical 6G scenarios such as intelligent metasurfaces and digital twins.
[0003] However, in practical applications, the operating airspace of UAVs often includes physical no-fly zones, such as airport airspace, military control zones, or airspace above sensitive infrastructure. Meanwhile, the increasingly complex electromagnetic warfare environment places higher demands on the stealth of wireless links, especially in military or high-security civilian scenarios, where communication processes must possess low probability of interception and low probability of detection. Currently, research on UAV trajectory planning and resource allocation still faces the following limitations: most works separate the sensing and communication functions, failing to fully leverage the potential of integrated systems in terms of spectrum, hardware, and power reuse; existing solutions are mostly based on discrete-time or fixed-hover point models, failing to effectively utilize the advantages of continuous trajectory control in improving system performance; and, more importantly, existing research rarely addresses the issues of stealth constraints and physical no-fly zone limitations within a unified framework.
[0004] Current 6G research emphasizes the design principle of "integrated communication and sensing," requiring deep integration of sensing and communication functions at the signal, resource, and network architecture levels. Traditional step-by-step optimization methods often isolate resource allocation and trajectory planning solution spaces, making it difficult to achieve global optimum while satisfying the dual constraints of stealth and no-fly zones. Especially when UAVs simultaneously perform downlink communication and radar target sensing tasks, their transmit power needs to be dynamically allocated between communication and sensing functions, and the power allocation strategy directly affects the stealth of communication and the accuracy of sensing. This multi-dimensional coupling relationship, together with continuous trajectory variables, constitutes a highly non-convex and nonlinear complex optimization problem that cannot be directly solved using conventional convex optimization methods.
[0005] Therefore, how to improve the covert communication capabilities and comprehensive perception accuracy of UAVs while meeting the needs of covert communication and the constraints of physical no-fly zones is an urgent problem to be solved. Summary of the Invention
[0006] To address the shortcomings of existing technologies, the purpose of this application is to provide a method for joint scheduling of UAV sensor resources in covert communication scenarios, which can improve the covert communication capabilities and comprehensive perception accuracy of UAVs while meeting the covert communication requirements and physical no-fly zone constraints.
[0007] To achieve the above objectives, firstly, this application provides a method for joint scheduling of UAV sensory resources in covert communication scenarios, comprising the following steps:
[0008] S10, pre-establish communication performance models of UAVs to multiple ground users and radar perception performance models to a single ground target; S20. Based on the aforementioned communication performance model and radar perception performance model, and combined with covert communication constraints and no-fly zone avoidance constraints, a joint optimization problem of communication and perception resources with the goal of maximizing total user throughput is constructed. S30, by solving the closed-form optimal solution of sensing power and communication power that satisfies radar sensing performance constraints and total power constraints, the joint optimization problem is reduced by variables; S40 introduces a segmented hovering flight structure, discretizing the continuous flight trajectory of the UAV into a series of hovering points and their corresponding hovering times. Combined with the closed-form optimal solution, the joint optimization problem is transformed into a discrete optimization problem concerning hovering points, hovering times, and user association. S50 uses the block coordinate descent algorithm to solve the discrete optimization problem iteratively, and outputs the optimal user association strategy, UAV hovering time and UAV continuous flight trajectory.
[0009] As a further preferred embodiment, in step S10, a communication performance model of the UAV to multiple ground users is established, specifically including: The system includes a drone with integrated sensing and communication capabilities and K One ground user; then the user k At any moment t Received signal-to-noise ratio for:
[0010] in, For the communication transmission power of the drone, For users Channel gain between drones and other devices For the drone's sensing transmission power. This represents the power of additive white Gaussian noise. Based on the aforementioned signal-to-noise ratio, the user k At any moment t Communication rate for:
[0011] in, B Indicates communication bandwidth; Definition of the first k Individual users during task time T Total throughput is a communication performance indicator:
[0012] in, For drones at all times t The trajectory position vector; This is a power vector that includes both communication and sensing transmit power; Associate binary variables for users.
[0013] As a further preferred option, in step S10, a radar perception performance model for a single ground target is established, specifically including: Set the ground target location as To ensure sensing accuracy, a radar sensing model is constructed and the instantaneous sensing signal-to-noise ratio is constrained as follows:
[0014] in, Represents the radar cross-section of the target; The operating wavelength of the radar; For radar transmitting antenna gain; This refers to the gain of the radar receiving antenna. Represents the Boltzmann constant; Effective system noise temperature; Radar noise figure; Reflects detection loss; The minimum sensing signal-to-noise ratio threshold required to maintain reliable sensing performance.
[0015] As a further preferred embodiment, in step S20, the covert communication constraints and no-fly zone avoidance constraints specifically include: The concealment performance safety constraints are characterized as follows:
[0016] in, This represents the uncertainty range of the noise power. The channel gain between the UAV and the ground monitoring point; A threshold is required for concealment; The safety constraints of a no-fly zone are characterized as follows: For the n A no-fly zone, the center of which is The minimum safe distance is Then the drone trajectory must satisfy: .
[0017] As a further preferred embodiment, step S30 involves solving for the closed-form optimal solution of the sensing power and the communication power, specifically including: Based on radar sensing signal-to-noise ratio constraints and the upper limit of UAV total transmit power Constraints to obtain sensing power With communication power Regarding drone trajectories The closed-form optimal solution is as follows:
[0018]
[0019] in, and The functions related to channel state and relative distance are defined as follows:
[0020] .
[0021] As a further preferred embodiment, in step S40, a segmented hovering flight structure is introduced to discretize the continuous flight trajectory, specifically including: Continuous trajectory Discretized into a series of hovering points Discretized into a series of hovering points and inflection point and their corresponding hovering time and at maximum speed between hovering points The flight path.
[0022] As a further preferred embodiment, in step S40, the joint optimization problem is transformed into a discrete optimization problem concerning the hovering point, hovering time, and user association, specifically as follows: Based on the closed-form optimal solution and the segmented hovering flight structure, the original joint optimization problem is transformed into optimization problem P2, whose objective function is to maximize the total throughput of all ground users. The expression is as follows:
[0023] in, Indicates that the drone is in i A hovering point and the user k Related variables; Indicates that the drone in the i The first hovering point j A turning point and usersk Related variables; The distance between the two inflection points. ; For any position of the drone between the two inflection points, , .
[0024] As a further preferred embodiment, step S50 employs a block coordinate descent algorithm for alternating iterative solution, specifically including: S501, Initialization: Set the iteration count index Maximum number of iterations Iteration accuracy threshold And set the initial feasible drone hovering point positions. User association strategy and hovering time ; S502, Solving the first subproblem: Fixing the hovering position of the drone in the current iteration. User association strategy Solve the convex optimization subproblem with hover time t as the variable, and update the hover time. ; S503, Solving the second subproblem: Fixing the hovering position of the drone in the current iteration. and hovering time Solve the linear integer programming subproblem with user association policy 'a' as the variable, and update the user association policy to obtain the updated policy. ; S504, Solving the third subproblem: Fixing the user association strategy in the current iteration and hovering time Solve the nonconvex optimization subproblem with the UAV hovering position q as the variable, and update the UAV hovering position using a continuous convex approximation method. ; S505, Iteration Judgment and Output: Determine whether the iteration termination condition is met; if met, output the current optimal solution as the optimal user association strategy, drone hovering time, and drone continuous flight trajectory; if not met, update the iteration count index. Then return to step S502 to continue the iteration.
[0025] As a further preferred embodiment, in step S505, the iteration termination condition is: the current iteration number. Reaching the maximum number of iterations Or, the absolute value of the difference between the objective function values obtained from two adjacent iterations is less than the iteration accuracy threshold. .
[0026] Secondly, this application provides a joint scheduling system for UAV sensor resources in covert communication scenarios, used to implement the method described in any one of the above statements, the system comprising: The model building module is used to pre-build communication performance models of UAVs to multiple ground users and radar perception performance models to a single ground target. The problem construction module is used to construct a joint optimization problem of communication and sensing resources with the goal of maximizing the total user throughput, based on the communication performance model and radar perception performance model, combined with covert communication constraints and no-fly zone avoidance constraints. The problem simplification module is used to reduce variables by solving the closed-form optimal solution of sensing power and communication power that satisfies radar sensing performance constraints and total power constraints, and introduces a segmented hovering flight structure to discretize the continuous flight trajectory of the UAV, thereby transforming the joint optimization problem into a discrete optimization problem concerning hovering point, hovering time and user association. The solution output module is used to solve the discrete optimization problem iteratively using the block coordinate descent algorithm, and outputs the optimal user association strategy, UAV hovering time, and UAV continuous flight trajectory.
[0027] The beneficial effects of this application are as follows: The UAV sensing resource joint allocation method provided in this application first derives and constructs a joint optimization problem of integrated communication and sensing resources that satisfies covert communication requirements and no-fly zone avoidance constraints (P1); then, based on radar sensing signal-to-noise ratio constraints and total power constraints, it obtains the closed-form optimal solution of communication power and sensing power with respect to the UAV trajectory, and transforms the continuous-time complex problem into a discrete optimization problem by introducing a segmented hovering flight structure (P2); finally, it uses a block coordinate descent algorithm to decompose the problem into three sub-problems concerning hovering time, user association, and flight trajectory, and solves them iteratively, outputting the optimal user association strategy, UAV hovering time, and continuous flight trajectory. This method effectively improves the covert communication performance and radar sensing accuracy of the UAV system while satisfying covert communication requirements and physical no-fly zone constraints, achieving efficient collaboration and joint optimization of communication and sensing functions in multiple dimensions of resources such as power, time, and space. Attached Figure Description
[0028] Figure 1 This is a flowchart of the UAV sensor resource joint scheduling method in covert communication scenarios provided in this application embodiment; Figure 2 This is a system model diagram of the method provided in the embodiments of this application; Figure 3 This is a flowchart illustrating the specific implementation of this method as provided in an embodiment of this application; Figure 4This is a performance comparison chart between the implementation of this application and the initial data scheme. Detailed Implementation
[0029] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.
[0030] like Figure 1 and 2 As shown, this application provides a method for joint scheduling of UAV sensory resources in covert communication scenarios, including the following steps: S1: Derive the downlink communication model between the UAV and multiple ground users; S2: Derive the radar perception model of the UAV for a single ground target; S3: Based on the communication performance model and the perception performance model, under the constraints of meeting the requirements of covert communication and avoiding no-fly zones, construct the joint optimization problem P1 of UAV communication and perception resources; S4: By solving the closed-form solution of communication power and sensing power, and introducing a segmented hovering flight structure, problem P1 is simplified into problem P2; S5: The block coordinate descent algorithm is used to solve the problem and output the optimal user association strategy, drone hovering time and drone trajectory.
[0031] The beneficial effects of this application are as follows: The UAV sensing resource joint allocation method provided in this application first derives and constructs a joint optimization problem of integrated communication and sensing resources that satisfies covert communication requirements and no-fly zone avoidance constraints (P1); then, based on radar sensing signal-to-noise ratio constraints and total power constraints, it obtains the closed-form optimal solution of communication power and sensing power with respect to the UAV trajectory, and transforms the continuous-time complex problem into a discrete optimization problem by introducing a segmented hovering flight structure (P2); finally, it uses a block coordinate descent algorithm to decompose the problem into three sub-problems concerning hovering time, user association, and flight trajectory, and solves them iteratively, outputting the optimal user association strategy, UAV hovering time, and continuous flight trajectory. This method effectively improves the covert communication performance and radar sensing accuracy of the UAV system while satisfying covert communication requirements and physical no-fly zone constraints, achieving efficient collaboration and joint optimization of communication and sensing functions in multiple dimensions of resources such as power, time, and space.
[0032] Furthermore, step S1 specifically includes: The system includes a drone with integrated sensing and communication capabilities and One ground user; then the user At any moment The received signal-to-noise ratio is:
[0033] in, For the communication transmission power of the drone, For users Channel gain between drones and other devices For the drone's sensing transmission power. This represents the power of additive white Gaussian noise.
[0034] Based on the above signal-to-noise ratio formula, the user The communication rates are as follows:
[0035] in, Indicates communication bandwidth.
[0036] Definition of the first Individual users during task time Total throughput is a communication performance indicator:
[0037] in, For the drone trajectory, For power vectors, Associate binary variables for users.
[0038] Furthermore, step S2 specifically includes: The ground target location is set as To ensure sensing accuracy, a radar sensing model is constructed and the instantaneous signal-to-noise ratio is constrained as follows:
[0039] in, Represents the radar cross-section (RCS) of the target; For radar wavelength, and These are the antenna gains at the radar transmitter and receiver, respectively. Represents the Boltzmann constant; Effective system noise temperature; Radar noise figure; This reflects the detection loss. Parameter The minimum signal-to-noise ratio threshold required to maintain reliable sensing performance is defined; Furthermore, step S3 specifically includes: The aforementioned concealment performance safety constraints are characterized as follows:
[0040] in, This represents the uncertainty range of the noise power. The channel gain between the UAV and the ground monitoring point; A threshold is required for concealment; Construct a no-fly zone constraint model. Define the first... The center of the no-fly zone is The minimum safe distance is The safety constraints of no-fly zones are represented by the following inequality:
[0041] Based on the above modeling, the UAV covert sensing joint optimization problem P1 is constructed as follows:
[0042] Furthermore, step S4 specifically includes: S41. Variable Reduction: Based on radar signal-to-noise ratio constraints and total power constraints, obtain the sensing power and communication power with respect to the UAV trajectory. The closed-form optimal solution is as follows:
[0043]
[0044] in, This is the upper limit for the sensor transmission power of drones; and The functions related to channel state and distance are as follows:
[0045]
[0046] S42. Trajectory Discretization: Introducing a segmented hovering flight structure to discretize the continuous trajectory. Discretized into a series of hovering points and inflection point and their corresponding hovering time and at maximum speed between hovering points Flight path; S43. Problem Transformation: Based on S41 and S42, the original problem P1 is simplified into an optimization problem P2 concerning hover point, hover time, and user association:
[0047] in:
[0048] Furthermore, step S5 uses a block coordinate descent algorithm to solve problem P2, obtaining the trajectories of multiple UAVs, user associations, and hovering times, such as... Figure 3 As shown, specifically: S51. Initialize the number of iterations Set the maximum number of iterations. Iteration accuracy threshold Set an initial feasible solution ; S52: Fixed Solve for hover time Sub-problems, updated to obtain ; S53: Fixed Seeking solutions regarding user associations. Sub-problems, updated to obtain ; S54: Fixed Solve for the flight trajectory Sub-problems, updated to obtain ; S55: Determine if the condition is met. If the value is less than the iteration threshold, or if so, the optimal solution is obtained. Based on this, a continuous trajectory is generated; otherwise, the iteration count is updated. And repeat steps S52-S55.
[0049] Figure 4 The performance of the optimized data scheme provided in this application embodiment is compared with that of the initial data scheme, and the designed scheme is verified by simulation using Matlab. Specifically, the parameters are set as follows: system bandwidth is set to... Maximum flight speed is The carrier wavelength is Maximum transmission power Total Time Both the transmit and receive antenna gains are 1. Minimum safe distance setting The radar signal-to-noise ratio threshold and communication security requirements are set as follows: and .
[0050] Figure 4 This section compares the minimum communication rate performance guaranteed by the system for different user IDs. The horizontal axis represents the user ID, and the vertical axis represents the minimum transmission rate guaranteed by the system for that user.
[0051] from Figure 4 It is clear from the data analysis that in the initial data scheme, different users experience significantly different transmission rates, demonstrating a clear imbalance in resource allocation. In contrast, under the optimized scheme proposed in this application, the transmission rates of all users remain consistent, resulting in an overall improvement in user communication performance and achieving a higher and more balanced level.
[0052] This result demonstrates that the proposed optimization method significantly improves the fairness of multi-user communication through intelligent resource scheduling and collaborative design. Under the premise of satisfying system constraints, it effectively guarantees the lower limit of service quality for all users, reflecting the superiority of the solution in terms of resource allocation fairness and overall system efficiency.
[0053] Through the preceding performance simulation comparisons, the method of this invention improves the communication and sensing performance of unmanned systems, achieving efficient scheduling of UAV swarm resources in the frequency, energy, and airspace domains. It is foreseeable that the method of this invention will be well-suited for future UAV-based communication and sensing technologies, thereby enhancing the overall performance of the swarm.
[0054] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the protection scope of this application.
Claims
1. A method for joint scheduling of sensing resources of UAVs in a covert communication scenario, the method comprising: Includes the following steps: S10, pre-establish communication performance models of UAVs to multiple ground users and radar perception performance models to a single ground target; S20. Based on the aforementioned communication performance model and radar perception performance model, and combined with covert communication constraints and no-fly zone avoidance constraints, a joint optimization problem of communication and perception resources with the goal of maximizing total user throughput is constructed. S30, by solving the closed-form optimal solution of sensing power and communication power that satisfies radar sensing performance constraints and total power constraints, the joint optimization problem is reduced by variables; S40 introduces a segmented hovering flight structure, discretizing the continuous flight trajectory of the UAV into a series of hovering points and their corresponding hovering times. Combined with the closed-form optimal solution, the joint optimization problem is transformed into a discrete optimization problem concerning hovering points, hovering times, and user association. S50 uses the block coordinate descent algorithm to solve the discrete optimization problem iteratively, and outputs the optimal user association strategy, UAV hovering time and UAV continuous flight trajectory. 2.The method of claim 1, wherein, In step S10, a communication performance model for the UAV to multiple ground users is established, specifically including: The system includes a drone with integrated sensing and communication capabilities and K One ground user; then the user k At any moment t Received signal-to-noise ratio for: in, For the communication transmission power of the drone, For users Channel gain between drones and other devices For the drone's sensing transmission power. This represents the power of additive white Gaussian noise. Based on the aforementioned signal-to-noise ratio, the user k At any moment t Communication rate for: in, B Indicates communication bandwidth; Definition of the first k Individual users during task time T Total throughput is a communication performance indicator: in, For drones at all times t The trajectory position vector; This is a power vector that includes both communication and sensing transmit power; Associate binary variables for users.
3. The method for joint scheduling of UAV sensory resources in covert communication scenarios as described in claim 1, characterized in that, In step S10, a radar perception performance model for a single ground target is established, specifically including: Set the ground target location as To ensure sensing accuracy, a radar sensing model is constructed and the instantaneous sensing signal-to-noise ratio is constrained as follows: in, Represents the radar cross-section of the target; The operating wavelength of the radar; For radar transmitting antenna gain; This refers to the gain of the radar receiving antenna. Represents the Boltzmann constant; Effective system noise temperature; Radar noise figure; Reflects detection loss; The minimum sensing signal-to-noise ratio threshold required to maintain reliable sensing performance.
4. The method for joint scheduling of UAV sensory resources in covert communication scenarios as described in claim 1, characterized in that, In step S20, the constraints on covert communication and no-fly zone avoidance specifically include: The concealment performance safety constraints are characterized as follows: in, This represents the uncertainty range of the noise power. The channel gain between the UAV and the ground monitoring point; A threshold is required for concealment; The safety constraints of a no-fly zone are characterized as follows: For the n A no-fly zone, the center of which is The minimum safe distance is Then the drone trajectory must satisfy: .
5. The method for joint scheduling of UAV sensory resources in covert communication scenarios as described in claim 1, characterized in that, In step S30, the closed-form optimal solution for sensing power and communication power is obtained, specifically including: Based on radar sensing signal-to-noise ratio constraints and the upper limit of UAV total transmit power Constraints to obtain sensing power With communication power Regarding drone trajectories The closed-form optimal solution is as follows: in, and The functions related to channel state and relative distance are defined as follows: 。 6. The method for joint scheduling of UAV sensory resources in covert communication scenarios as described in claim 1, characterized in that, In step S40, a segmented hovering flight structure is introduced to discretize the continuous flight trajectory, specifically including: Continuous trajectory Discretized into a series of hovering points Discretized into a series of hovering points and inflection point and their corresponding hovering time and at maximum speed between hovering points The flight path.
7. The method for joint scheduling of UAV sensory resources in covert communication scenarios as described in claim 6, characterized in that, In step S40, the joint optimization problem is transformed into a discrete optimization problem concerning the hovering point, hovering time, and user association, specifically as follows: Based on the closed-form optimal solution and the segmented hovering flight structure, the original joint optimization problem is transformed into optimization problem P2, whose objective function is to maximize the total throughput of all ground users. The expression is as follows: in, Indicates that the drone is in i A hovering point and the user k Related variables; Indicates that the drone in the i The first hovering point j A turning point and users k Related variables; The distance between the two inflection points. ; For any position of the drone between the two inflection points, , .
8. The method for joint scheduling of UAV sensory resources in covert communication scenarios as described in claim 1, characterized in that, In step S50, the block coordinate descent algorithm is used for alternating iterative solution, specifically including: S501, Initialization: Set the iteration count index Maximum number of iterations Iteration accuracy threshold And set the initial feasible drone hovering point positions. User association strategy and hovering time ; S502, Solving the first subproblem: Fixing the hovering position of the drone in the current iteration. User association strategy Solve the convex optimization subproblem with hover time t as the variable, and update the hover time. ; S503, Solving the second subproblem: Fixing the hovering position of the drone in the current iteration. and hovering time Solve the linear integer programming subproblem with user association policy 'a' as the variable, and update the user association policy to obtain the updated policy. ; S504, Solving the third subproblem: Fixing the user association strategy in the current iteration and hovering time Solve the nonconvex optimization subproblem with the UAV hovering position q as the variable, and update the UAV hovering position using a continuous convex approximation method. ; S505, Iteration Judgment and Output: Determine whether the iteration termination condition is met; if met, output the current optimal solution as the optimal user association strategy, drone hovering time, and drone continuous flight trajectory; if not met, update the iteration count index. Then return to step S502 to continue the iteration.
9. The method for joint scheduling of UAV sensory resources in covert communication scenarios as described in claim 8, characterized in that, In step S505, the iteration termination condition is: the current iteration number. Reaching the maximum number of iterations Or, the absolute value of the difference between the objective function values obtained from two adjacent iterations is less than the iteration accuracy threshold. .
10. A joint scheduling system for UAV sensor resources in covert communication scenarios, characterized in that, The system for implementing the method as described in any one of claims 1 to 9 comprises: The model building module is used to pre-build communication performance models of UAVs to multiple ground users and radar perception performance models to a single ground target. The problem construction module is used to construct a joint optimization problem of communication and sensing resources with the goal of maximizing the total user throughput, based on the communication performance model and radar perception performance model, combined with covert communication constraints and no-fly zone avoidance constraints. The problem simplification module is used to reduce variables by solving the closed-form optimal solution of sensing power and communication power that satisfies radar sensing performance constraints and total power constraints, and introduces a segmented hovering flight structure to discretize the continuous flight trajectory of the UAV, thereby transforming the joint optimization problem into a discrete optimization problem concerning hovering point, hovering time and user association. The solution output module is used to solve the discrete optimization problem iteratively using the block coordinate descent algorithm, and outputs the optimal user association strategy, UAV hovering time, and UAV continuous flight trajectory.